Abstract: Some embodiments of the disclosure provide a decellularized heart valve (DHV) composite, a preparation method of the DHV composite, and a use of the DHV composite. In some examples, the preparation method of the DHV composite includes the following steps: S1, conducting a reaction I on a DHV with a copper chloride-dopamine hydrochloride mixed solution to obtain a copper ion-modified DHV; and S2, conducting a reaction II on the copper ion-modified DHV with a GDF11 solution to obtain the DHV composite. In other examples, the present disclosure provides a use of the DHV composite in preparation of a tissue-engineered heart valve (TEHV). In further examples, the TEHV is a heart valve (HV) with remodeling and regeneration capabilities.

Abstract: A (methyl)acrolein oxidation catalyst and a preparation method therefor-in which the catalyst has a composition represented by the following formula: x(Mo12PaCsbVcDeOf)+tC/yZ in which Mo12PaCSbVcDeOf is a heteropolyacid salt main catalyst; C is a nano carbon fiber additive, and Z is a carrier thermal conduction diluent; Mo, P, Cs, V, and O represent the elements of molybdenum, phosphorus, cesium, vanadium, and oxygen, respectively; D represents at least one element selected from the group consisting of copper, iron, magnesium, manganese, antimony, zinc, tungsten, silicon, nickel, and palladium; a, b, c, e, and f represent the atomic ratio of each element, a=0.1-3, b=0.01-3, c=0.01-5, e=0.01-2, and f being the atomic ratio of oxygen required to satisfy the valence of each of the described components; x and y represent the weights of the main catalyst and the carrier thermal conduction diluent Z, and y/x=11.1-50%; and t represents the weight of the nano carbon fiber, and t/x=3-10%.

Abstract: Embodiments of the present disclosure provide systems, methods, and apparatuses for addressing the above problems through the use of access rules that involve analyzing historical access request result data for various data elements individually and in combination over a predefined time interval. An automated determination can be made for whether a transaction can be authenticated based upon the historical access request result data (e.g., ultimately, deciding whether the data element or set of data elements are associated with a valid access request).

Abstract: Disclosed is a system to optimize rule weights for classifying access requests so as to manage rates of false positives and false negative classifications. A rules suggestion engine may suggest a profile of classification rules to a merchant for access requests. The system can optimize weights for the profile of rules using a cost function based on a training set of historical access requests, for example using stepwise regression or machine learning (ML). The system can compute a profile score based on the optimized weights, for example by summing the weights. The system statistically analyzes the profile score using classification thresholds and the historical access requests. The system can perform receiver operating characteristic (ROC) analysis for various threshold values, enabling a user to select a suitable threshold. The system can further optimize by adding or removing rules from the profile of rules.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Disclosed is a system to optimize rule weights for classifying access requests so as to manage rates of false positives and false negative classifications. A rules suggestion engine may suggest a profile of classification rules to a merchant for access requests. The system can optimize weights for the profile of rules using a cost function based on a training set of historical access requests, for example using stepwise regression or machine learning (ML). The system can compute a profile score based on the optimized weights, for example by summing the weights. The system statistically analyzes the profile score using classification thresholds and the historical access requests. The system can perform receiver operating characteristic (ROC) analysis for various threshold values, enabling a user to select a suitable threshold. The system can further optimize by adding or removing rules from the profile of rules.

Abstract: Disclosed is a system to optimize rule weights for classifying access requests so as to manage rates of false positives and false negative classifications. A rules suggestion engine may suggest a profile of classification rules to a merchant for access requests. The system can optimize weights for the profile of rules using a cost function based on a training set of historical access requests, for example using stepwise regression or machine learning (ML). The system can compute a profile score based on the optimized weights, for example by summing the weights. The system statistically analyzes the profile score using classification thresholds and the historical access requests. The system can perform receiver operating characteristic (ROC) analysis for various threshold values, enabling a user to select a suitable threshold. The system can further optimize by adding or removing rules from the profile of rules.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Described are a system, method, and computer program product for optimizing a predictive condition classification model and automatically enacting reactive measures based thereon. The method includes receiving event data representative of a plurality of events. The method also includes receiving the predictive condition classification model configured to categorize each event as satisfying a condition or not. The predictive condition classification model is configured to order the plurality of events by likelihood of satisfying the condition. The method includes generating a performance evaluation dataset and plotting data configured to cause a visual display to represent at least two model performance metrics of the performance evaluation dataset on a same output plot. The method includes automatically rejecting a top percent of the plurality of events for suspected satisfaction of the condition, determined at least partially from a customized rejection algorithm or a preset rejection algorithm.

Abstract: Disclosed is a system to optimize rule weights for classifying access requests so as to manage rates of false positives and false negative classifications. A rules suggestion engine may suggest a profile of classification rules to a merchant for access requests. The system can optimize weights for the profile of rules using a cost function based on a training set of historical access requests, for example using stepwise regression or machine learning (ML). The system can compute a profile score based on the optimized weights, for example by summing the weights. The system statistically analyzes the profile score using classification thresholds and the historical access requests. The system can perform receiver operating characteristic (ROC) analysis for various threshold values, enabling a user to select a suitable threshold. The system can further optimize by adding or removing rules from the profile of rules.

Abstract: Embodiments of the present disclosure provide systems, methods, and apparatuses for addressing the above problems through the use of access rules that involve analyzing historical access request result data for various data elements individually and in combination over a predefined time interval. An automated determination can be made for whether a transaction can be authenticated based upon the historical access request result data (e.g., ultimately, deciding whether the data element or set of data elements are associated with a valid access request).

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Described are a system, method, and computer program product for optimizing a predictive condition classification model and automatically enacting reactive measures based thereon. The method includes receiving event data representative of a plurality of events. The method also includes receiving the predictive condition classification model configured to categorize each event as satisfying a condition or not. The predictive condition classification model is configured to order the plurality of events by likelihood of satisfying the condition. The method includes generating a performance evaluation dataset and plotting data configured to cause a visual display to represent at least two model performance metrics of the performance evaluation dataset on a same output plot. The method includes automatically rejecting a top percent of the plurality of events for suspected satisfaction of the condition, determined at least partially from a customized rejection algorithm or a preset rejection algorithm.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: Resources can be secured by a resource security system. The resource security system can determine whether to grant or deny access to resources using authorization information in an access request. The resource security system can also determine whether the access request is legitimate or fraudulent using risk scoring models. A score transformation table can be used to provide consistency in the risk level for a particular score over time. The score transformation table can be based on a target score profile and a precision format (e.g., integer or floating point). The score transformation table can dynamically adapt based on the trending top percent of risk and can account for changes in the distribution of scores over time or by weekday. The scores can be used to determine an access request outcome. Access to the resource can be accepted or rejected based on the outcome.

Abstract: A data structure provides reliable data allowing a security application to detect potential instances of fraudulent use of a payment account. The data structure can be generated using data elements associated with transactions from new authentication requests in a transaction. Once the data structure is generated, clusters within the data structure can be associated with legitimate authentication requests or potentially fraudulent authentication requests. A baseline cluster can be identified from the data structure and used to determine whether the new incoming authentication requests are legitimate or potentially fraudulent.